With the development of communication techniques, device positioning in a networking environment has been paid more and more attention, especially in places where Global Position System (GPS) is inadequate due to various causes such as multipath, signal blockage indoors, and so on. Among conventional solutions for device positioning, due to ubiquity of access points (APs) in buildings and urban areas, Wireless Fidelity (WiFi) positioning is becoming increasingly common for a variety of devices. Examples of such devices include, but are not limited to, mobile phones, tablets, personal digital assistants (PDAs), laptops, smart cameras, smart watches, smart glasses, and the like.
Conventionally, positioning schemes are generally classified into statistic schemes and dynamic schemes. The statistic schemes, such as propagation model approaches, determine positions of the devices according to three-point positioning methods and known positions of APs. Positioning accuracy of the statistic schemes depend on propagation attenuation models and thus is difficult to be guaranteed in different scenarios. The dynamic schemes, such as location fingerprints approaches, generally includes two stages, namely, an offline stage and an online stage. At the offline stage, a knowledge base is constructed. At the online stage, a radio map is dynamically generated based on the knowledge base, and a positioning system determines positions of the devices based on measured signal strength and the radio map. The dynamic schemes could capture time-adaptation of wireless signals and have better accuracy, thus they are widely used in practice. However, in a positioning system employing the dynamic schemes, users are generally required to additionally install a dedicated application (APP) on the devices.